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As smart cities become a global topic, interest in smart mobility, the core of smart cities, is also growing. The technology that comes closest to general users is “autonomous driving”. In particular, the successful market entry and establishment of some private companies proved that “autonomous driving” is not technology of the future but imminent reality. However, safety in autonomous vehicles that rely on sensors instead of the driver’s five senses has been the focus of attention from the beginning and continues to be so. In this study, we attempted to counter this interest. Based on the actual data of thirty traffic accidents, assuming the AEBS (Autonomous Emergency Braking System) was installed to assist the driver in safe driving, it was reinterpreted through simulation to see what changes occurred in the accident. In the computer program, PC-Crash, the results were first analyzed through simulation using Euro NCAP (New Car Assessment Program)’s AEBS test standards. Subsequently, the other variables in the AEBS were controlled and the accident was reinterpreted by changing only the angle of the radar detection sensor. As a result, it was confirmed that a total of 27 accidents out of thirty accidents could have been prevented with the AEBS. In addition, it proved that the crash avoidance rate of vehicles gradually increased as the radar angle increased.

Purpose This study aims to consider environmental sustainability, a global challenge under the preview of sustainable development goals, highlighting the significance of knowledge economy in attaining sustainable aggregate demand behavior globally. For this purpose, 155 countries that have data available from 1995 to 2021 were selected. The purpose of selecting these countries is to test the global responsibility of the knowledge economy to attain environmental sustainability. Design/methodology/approach Results are estimated with the help of panel quantile regression. The empirical existence of aggregate demand-based environmental Kuznets curve (EKC) was tested using non-linear tests. Moreover, principal component analysis has been incorporated to construct the knowledge economy index. Findings U-shaped aggregate demand-based EKC at global level is validated. However, environmental deterioration increases with an additional escalation after US$497.945m in aggregate demand. As a determinant, the knowledge economy is reducing CO2 emissions. The knowledge economy has played a significant role in global responsibility, shifting the EKC downward and extending the CO2 reduction phase for every selected country. Further, urbanization, energy intensity, financial development and trade openness significantly deteriorate the environmental quality. Originality/value This study contains the empirical existence of aggregate demand-based EKC. The role of the knowledge economy is examined through an index which is calculated by using four pillars of the knowledge economy (technology, innovations, education and institutions). This study is based on a combined panel of all the countries for which the data was available.

Owing to the growing interest of bioconvection flow of nanomaterials, many investigations on this topic have been performed, especially in this decade. The bioconvection flow of nanofluid includes some novel significance in era of biotechnology and bio-engineering like bio-fuels, microbial enhanced oil recovery, enzymes, pharmaceutical applications, petroleum engineering, etc. The current analysis aims to explore the various thermal properties of Sutterby nanofluid over rotating and stretchable disks with external consequences of variable thermal conductivity, heat absorption/generation consequences, activation energy and thermal radiation. The considered flow problem is changed into dimensionless form with convenient variables. The numerical structure for the obtained non-dimensional equations is numerically accessed with built-in shooting technique. The consequences of various physical parameters are observed for enhancement of velocity, temperature, concentration and motile microorganism. It is noted that both axial and tangential velocity components decrease with Reynolds number and buoyancy ratio parameter. The nanofluid concentration improves with activation energy and concentration Biot number. Moreover, an improved microorganisms profile is noticed with microorganism Biot number and bioconvection Rayleigh number.

Abstract The variation of properties with temperature have many practical applications in the area of chemical engineering and metallurgy, in the process of extrusion, the materials moving between a feed roll and on carrier belt. Here our intention is to explore the consequences of temperature dependent viscosity and variable thermal conductivity on flow of nanoliquid in a rotating system. Fluid between parallel plates is taken. Energy expression incorporates the exponential heat source. This analysis also explores the attributes of modified Arrhenius energy function with chemical reaction. Boundary layer problems have been computed via NDSolve approach. The obtained solutions are sketched for various estimations of embedded variables of interest. Results pointed out that both axial and transverse velocities show decaying behavior for higher rotation parameter. Thermal field boosts up with an enhancement of thermophoretic and space dependent heat source variables. Moreover the rate of mass transport decays through dimensionless activation energy.

Structural, optoelectronic, photovoltaic, and supplementary characterization data for “Benzylammonium-Mediated Formamidinium Lead Iodide Perovskite Phase Stabilization for Photovoltaics”, DOI:10.1002/adfm.202101163. Figure_2_XRD.zip: Data described in Figure 2 (XRD patterns) as Origin (.opj) software file. Figure_3_NMR_data.zip: Data described in Figure 3 (NMR spectra) in the file structure of the TopSpin software, which is available from Bruker. Figure_4_spectra.zip: Data described in Figure 4 (UV-vis absorption, PL and IPCE spectra) as Origin (.opj) software files. Figure_5_PV.zip: Data described in Figure 5 (photovoltaic characterization) as Origin (.opj) software files. Figure_6_spectra.zip: Data described in Figure 6 (PLQY and TRPL) as Origin (.opj) and *.csv files. Figure_7_stability.zip: Data described in Figure 7 (stability analysis) as Origin (.opj) software files. Figure_SI.zip: Data described in the Supporting Information Figures S1, S2, S3, S5, and S6 (XRD data, reciprocal space maps, radial profiles of q-maps, UV-vis absorption spectra, PL spectra, and additional photovoltaic characterization) as Origin (.opj), text (.txt), and image (.tiff) files.

Microbial fuel cell (MFC) has received much attention in the last decade as a promising technology to simultaneously generate electricity and decontaminate wastewater. This study aims to quantitatively review the published literature on MFC, published in the period of 1970–2020, based on the Web of Science (WoS) database. For the first time in literature, a comprehensive quantitative review of MFC has been conducted by employing the technique of bibliometric and content analyses. A total of 11,397 publications have been retrieved from WoS, out of which 81.6% are research articles. The evaluation in the field of MFC has been mapped in various categories, such as publication history, publication distribution, subject category distribution, leading journals, leading countries and leading organizations in MFC research. Additionally, content analysis has been conducted to unearth the research trends in MFC; and some hot research topics in MFC have been spotted. Results depict that the period 2011–2020 has been the most appreciating era for MFC research, as it contributed 87% of the total publications. Among the subject categories, energy fuel and microbiology lead with contributions of 26.5% for each, butthe overall growth of the energy fuel category in the last decade has been the highest. Out of 1,147 journals publishing MFC research, Bioresource Technology is the leading one; and countries like China, USA and India are the main hub of MFC research with 26.47%, 16.95% and 7.69% contributions in publications, respectively. The hottest topics in MFC research are nanoparticles, catalysts, air electrodes, graphene electrodes, power enhancement, air cathode and nitrogen removal. Moreover, major research areas are engineering, energy fuels and biotechnology with each contribution 26.5% of the total publications.

Microplastics are a growing concern in the environments and especially agricultural soil due to numerous negative impacts on the ecosystems and human health. This study aimed to investigate the properties of microplastics (including quantity, shape, size, and polymer types) in five samples (DA1-5) taken from agriculture soil in Dong Anh district, Hanoi using Nicolet iN10 MX Fourier transform infrared (µFTIR) microscope. The study found that the microplastic content in these soils ranging from 494 ± 292 items/kg dry weight to 1031 ± 379 items/kg dry weight. In terms of shape, microplastic fragments accounted for the majority of microplastics found in soil (65% - 86% of the total microplastics). Fifteen types of microplastic polymers were identified in the soil samples, with PET being the most common polymer of 38%, followed by urea-formaldehyde resin of 15%, and nylon of 13%. Microplastics with sizes ranging from 50-150 µm were the dominant group (39% - 57% of the total microplastics). This study provides an initial assessment of the presence of microplastics in the agricultural soil of the suburban area of Hanoi, highlighting the potential risk of contamination that may effect on environment and pose a threat to human health.

Room occupancy prediction based on indoor environmental quality may be the breakthrough to ensure energy efficiency and establish an interior ambience tailored to each user. Identifying whether temperature, humidity, lighting, and CO2 levels may be used as efficient predictors of room occupancy accuracy is needed to help designers better utilize the readings and data collected in order to improve interior design, in an effort to better suit users. It also aims to help in energy efficiency and saving in an ever-increasing energy crisis and dangerous levels of climate change. This paper evaluated the accuracy of room occupancy recognition using a dataset with diverse amounts of light, CO2, and humidity. As classification algorithms, K-nearest neighbors (KNN), hybrid Adam optimizer–artificial neural network–back-propagation network (AO–ANN (BP)), and decision trees (DT) were used. Furthermore, this research is based on machine learning interpretability methodologies. Shapley additive explanations (SHAP) improve interpretability by estimating the significance values for each feature for classifiers applied. The results indicate that the KNN performs better than the DT and AO-ANN (BP) classification models have 99.5%. Though the two classifiers are designed to evaluate variations in interpretations, we must ensure that they have accurate detection. The results show that SHAP provides successful implementation following these metrics, with differences detected amongst classifier models that support the assumption that model complexity plays a significant role when predictability is taken into account.